Abstract
Introduction
Left ventricle (LV) quantification in nuclear medicine images is a challenging task for myocardial perfusion scintigraphy. A hybrid method for left ventricle myocardial border extraction in SPECT datasets was developed and tested to automate LV ventriculography.
Methods
Automatic segmentation of the LV in volumetric SPECT data was implemented using a variational level set algorithm. The method consists of two steps: (1) initialization and (2) segmentation. Initially, we estimate the initial closed curves in SPECT images using adaptive thresholding and morphological operations. Next, we employ the initial closed curves to estimate the final contour by variational level set. The performance of the proposed approach was evaluated by comparing manually obtained boundaries with automated segmentation contours in 10 SPECT data sets obtained from adult patients. Segmented images by proposed methods were visually compared with manually outlined contours and the performance was evaluated using ROC analysis.
Results
The proposed method and a traditional level set method were compared by computing the sensitivity and specificity of ventricular outlines as well as ROC analysis. The results show that the proposed method can effectively segment LV regions with a sensitivity and specificity of 88.9 and 96.8%, respectively. Experimental results demonstrate the effectiveness and reasonable robustness of the automatic method.
Conclusion
A new variational level set technique was able to automatically trace the LV contour in cardiac SPECT data sets, based on the characteristics of the overall region of LV images. Smooth and accurate LV contours were extracted using this new method, reducing the influence of nearby interfering structures including a hypertrophied right ventricle, hepatic or intestinal activity, and pulmonary or intramammary activity.
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References
Reyes E, Underwood SR (2006) Myocardial perfusion scintigraphy. An important step between clinical assessment and coronary angiography in patients with stable chest pain. Eur Heart J 27: 3–4
Grosgeorge D, Petitjean C, Caudron J, Fares J, Nicolas Dacher J (2011) Automatic cardiac ventricle segmentation in MR images: a validation study. Int J Comput Assist Radiol Surg 6(5): 573–581
Marsousi M, Eftekhari A, Kocharian A, Alirezaie J (2010) Endocardial boundary extraction in left ventricular echocardiographic images using fast and adaptive B-spline snake algorithm. Int J Comput Assist Radiol Surg 5(5): 501–513
Debreuve E, Marlaud M, Aubert G, Darcourt J (2001) Space time segmentation using level set active contours applied to myocardial gated SPECT. IEEE Med Imaging 20(7): 643–659
Paragios N, Mellina-Gottardo O, Ramesh V (2004) Gradient vector flow fast geodesic active contours. IEEE PAMI 26(3): 402–407
Yanga R, Mirmehdia M, Hallb D (2006) A charged contour model for cardiac SPECT segmentation. Med Image Underst Anal 1: 1–5
Dornheim L, Tönnies KD, Dixon Kat (2005) Automatic segmentation of the left ventricle in 3D SPECT data by registration with a dynamic anatomic model. Med Image Comput Comput Assist Interv(MICCAI) 3749: 335–342
Warfield S, Dengler J, Zaers J, Guttmann C, Gil W, Ettinger G, Hiller J, Kikinis R (1995) Automatic identification of grey matter structures from MRI to improve the segmentation of white matter lesions. J Image Guid Surg 1(6): 326–338
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cyben 9(1): 62–66
Gonzales RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, New Jersey
Gonzales RC, Woods RE, Eddins SL (2003) Digital image processing using MATLAB, 1st edn. Prentice Hall, New Jersey
Kass M, Witkin A, Terozopolous D (1988) Snakes: active contour models. Int J Comput Vis 1(4): 321–331
Delingette H, Montagnat J (2001) Shape and topology constraints on parametric active contours. Comput Vis Image Underst 83: 140–171
Yezzi A, Kichenassamy S, Kumar A, Olver P, Tannenbaum A (1997) A geometric snake model for segmentation of medical imagery. IEEE Trans Med Imag 16: 199–209
Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79: 12–49
Zhao HK, Chan TF, Merriman B, Osher S (1996) A variational level set approach to multiphase motion. J Comput Phys 127(1): 79–195
Vese L, Chan T (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vision 50(3): 271–293
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3): 297–302
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognt Lett 27(8): 861–874
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Hosntalab, M., Babapour-Mofrad, F., Monshizadeh, N. et al. Automatic left ventricle segmentation in volumetric SPECT data set by variational level set. Int J CARS 7, 837–843 (2012). https://doi.org/10.1007/s11548-012-0770-x
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DOI: https://doi.org/10.1007/s11548-012-0770-x